Description
This document provides links to step-by-step instructions on how to leverage Reference model docker containers to run optimized open-source Deep Learning Training and Inference workloads using PyTorch* framework on 4th Generation Intel® Xeon® Scalable processors
Note The containers below are based on pre-production build for Intel® Extenstion for PyTorch* and are for customer preview only and are not intended for use in production.
Use cases
The tables below provide links to run each use case using docker containers. The model scripts run on Linux.
Image Recognition
| Model | Model Documentation | Dataset |
|---|---|---|
| ResNet 50 | Training | ImageNet 2012 |
| ResNet 50 | Inference | ImageNet 2012 |
| ResNext-32x16d | Inference | ImageNet 2012 |
Object Detection
| Model | Model Documentation | Dataset |
|---|---|---|
| Mask R-CNN | Training | COCO 2017 |
| Mask R-CNN | Inference | COCO 2017 |
| SSD-ResNet34 | Training | COCO 2017 |
| SSD-ResNet34 | Inference | COCO 2017 |
Language Modeling
| Model | Model Documentation | Dataset |
|---|---|---|
| BERT large | Training | Preprocessed Text dataset |
| BERT large | Inference | SQuAD1.0 |
| RNN-T | Inference | RNN-T dataset |
| DistilBERT base | Inference | DistilBERT Base SQuAD1.1 |
Recommendation
| Model | Model Documentation | Dataset |
|---|---|---|
| DLRM | Training | Criteo Terabyte |
| DLRM | Inference | Criteo Terabyte |
Documentation and Sources
Get Started Code Sources
Main GitHub* Dockerfiles
License Agreement
LEGAL NOTICE: By accessing, downloading or using this software and any required dependent software (the “Software Package”), you agree to the terms and conditions of the software license agreements for the Software Package, which may also include notices, disclaimers, or license terms for third party software included with the Software Package. Please refer to the license file for additional details.